Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media Contexts (2025.acl-long)
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| Challenge: | Recent advances in Vision-Language Models (VLMs) have broadened the scope of multimodal applications, but evaluations often neglect abstract dimensions such as personality traits and human values. |
| Approach: | They propose a Visual Question Answering (VQA) benchmark based on Schwartz’s value dimensions that capture core human values guiding people’s preferences and actions. |
| Outcome: | The proposed model can be used to evaluate visual question answering (VQA) tasks and to simulate diverse personas. |
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| Challenge: | Existing studies on VLM bias focus on portrait-style images and gender-occupation associations . existing studies ignore broader and more complex social stereotypes and their implied harm . |
| Approach: | They propose a large-scale VQA benchmark for evaluating bias in vision-language models . they use a question-answering framework that spans factuality, perception, stereotyping, and decision making . |
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Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Models (2025.findings-naacl)
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| Challenge: | Using large vision-language models to understand cultural contexts is a critical area of research. |
| Approach: | They conduct a thorough evaluation of multimodal models at different scales, focusing on their alignment with cultural values. |
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Ask Me Again Differently: GRAS for Measuring Bias in Vision Language Models on Gender, Race, Age, and Skin Tone (2026.findings-eacl)
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| Challenge: | Using vision language models, we examine demographic biases in VLMs across gender, race, age, and skin tone. |
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Hospitality-VQA: Decision-Oriented Informativeness Evaluation for Vision–Language Models (2026.eacl-srw)
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Jeongwoo Lee, Baek Duhyeong, Eungyeol Han, Soyeon Shin, Gukin Han, Seungduk Kim, Jaehyun Jeon, Taewoo Jeong
| Challenge: | Existing VQA benchmarks focus on factual correctness but rarely capture what information users actually find useful. |
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ValueBench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) are transforming diverse fields and gaining increasing influence as human proxies. |
| Approach: | They propose a psychometric evaluation pipeline grounded in realistic human-AI interactions to probe value orientations and novel tasks for evaluating value understanding in an open-ended value space. |
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Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions (2025.acl-long)
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| Challenge: | Existing research addresses ambiguous visual questions by rephrasing questions, but it fails to address the inherently interactive nature of user interactions with visual language models (VLMs). Existing studies focus on re-phrase questions, and lack of a benchmark to assess VLMs’ capacity for resolving ambiguities through interaction. |
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VIVA: A Benchmark for Vision-Grounded Decision-Making with Human Values (2024.emnlp-main)
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| Challenge: | Recent large vision language models (VLMs) have demonstrated remarkable intelligence and proficiency across diverse tasks. |
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Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration (2025.acl-long)
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| Challenge: | Existing approaches to creating inclusive vision-language models rely on human annotators, making it labor-intensive and creating cognitive burdens. |
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A Unified Framework and Dataset for Assessing Societal Bias in Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Existing studies have highlighted the existence of social biases within large vision and language models. |
| Approach: | They propose a framework for systematically evaluating gender, race, and age biases in vision-language models with respect to professions. |
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AesBiasBench: Evaluating Bias and Alignment in Multimodal Language Models for Personalized Image Aesthetic Assessment (2025.emnlp-main)
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| Challenge: | Multimodal Large Language Models are increasingly used in Personalized Image Aesthetic Assessment (PIAA) however, their predictions may reflect subtle biases influenced by demographic factors such as gender, age, and education. |
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